What is a limitation of the Naive Bayes classifier?

The Naive Bayes classifier operates on the assumption that the features used to predict the outcome are independent of one another. This assumption simplifies the modeling process significantly and allows the classifier to work efficiently, especially in high-dimensional spaces. However, in many real-world scenarios, this independence assumption does not hold true. Features are often correlated; for example, in text classification, the presence of certain words may be related to the presence of other words. When features are dependent, the predictions made by the Naive Bayes classifier can be less accurate, leading to decreased performance on tasks where these correlations are significant.

This limitation highlights a crucial aspect of using Naive Bayes: while it is computationally efficient and works well with a large number of inputs and classes, the effectiveness heavily relies on the validity of its independence assumption. The failure of this assumption can lead to misclassifications and reflects one of the main reasons why practitioners often explore other classification methods when dealing with complex or correlated datasets.